DATAWAREHOUSING Pppt for data warehouin

Data warehousing ppt

1. What is Data Warehousing? • Centralized repository for integrated data\n• Used for reporting and data analysis

2. Why is Data Warehousing Used? • Improves decision-making\n• Supports business intelligence\n• Enables historical analysis

3. History of Data Warehousing • Emerged in the 1980s\n• Coined by Bill Inmon\n• Evolved with advancements in data storage and processing

4. Components of Data Warehousing • Data Sources\n• ETL Tools\n• Data Warehouse Database\n• BI Tools

5. Data Warehousing Architecture • Single-tier architecture\n• Two-tier architecture\n• Three-tier architecture

6. ETL Process • Extraction, Transformation, and Loading process\n• Critical for data quality

7. Data Warehousing vs. Databases • Databases: Operational data, transaction processing\n• Data Warehousing: Analytical data, decision support

8. Types of Data Warehouses • Enterprise Data Warehouse (EDW)\n• Operational Data Store (ODS)\n• Data Mart

9. OLAP in Data Warehousing • Online Analytical Processing\n• Multidimensional analysis\n• Cube structures

10. Data Marts • Subset of a data warehouse\n• Focused on a specific business area

11. Dimensional Modeling • Data modeling technique\n• Organizes data into facts and dimensions

12. Star Schema • Simple structure\n• Fact table connected to dimension tables\n• Commonly used in data warehousing

13. Snowflake Schema • More complex than Star Schema\n• Dimension tables are normalized

14. Fact and Dimension Tables • Fact tables: store quantitative data\n• Dimension tables: store descriptive data

15. Data Warehousing Tools • Tools: Informatica, Talend, AWS Redshift\n• BI tools: Tableau, Power BI

16. Data Warehousing Best Practices • Data quality management\n• Consistent data modeling\n• Scalable architecture

17. Challenges in Data Warehousing • Data integration\n• Scalability\n• Data security

18. Future Trends in Data Warehousing • Cloud-based data warehousing\n• Real-time data warehousing\n• AI and machine learning integration

19. Data Warehousing Case Studies • Success stories from various industries\n• Demonstrates the value of data warehousing

20. Conclusion • Recap of key points\n• Importance of data warehousing in modern businesses